| Metric | Value |
|---|---|
| Total Samples | 100 |
| Correct Predictions (all data) | 87 |
| Accuracy (all data) | 87.00% |
| Refined Accuracy | 87.00% |
| Parseable Accuracy | 87.00% (100/100 samples) |
| Unparseable Predictions | 0 (0.00%) |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 0 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 52.00% | 26 of 50 correct |
| 1 | Linear separation based on x and y | 0.00% | 0 of 50 correct |
| 2 | Distance from origin determines classification | 100.00% | 50 of 50 correct |
| 3 | Unit circle classification with more validation | 100.00% | 50 of 50 correct |
| 4 | Final unit circle classification rule | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [0.890, 0.135] | 1 | 0 | ✗ WRONG |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 1 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Distance-based classification with threshold at 0.9 | 96.00% | 48 of 50 correct |
| 1 | Refined distance-based classification with strict threshold at 0.9 | 96.00% | 48 of 50 correct |
| 2 | Angle-based classification | 48.00% | 24 of 50 correct |
| 3 | Product of coordinates (x*y) classification | 48.00% | 24 of 50 correct |
| 4 | Sum of coordinates (x+y) classification | 48.00% | 24 of 50 correct |
| 5 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 6 | 1-Nearest Neighbor classification | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [-0.618, -0.786] | 0 | ERROR | ✗ WRONG |
| [0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [-0.618, -0.786] | 0 | ERROR | ✗ WRONG |
| [0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 2 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Exact match lookup | 58.00% | 29 of 50 correct |
| 1 | Positive y-axis with lower x values → Class 1 | 58.00% | 29 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 3 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 56.00% | 28 of 50 correct |
| 1 | Classification based on sum or product of features | 0.00% | 0 of 50 correct |
| 2 | Classification based on ratio of y/x | 0.00% | 0 of 50 correct |
| 3 | Classification based on a diagonal line | 0.00% | 0 of 50 correct |
| 4 | Classification based on distance from origin (unit circle) | 100.00% | 50 of 50 correct |
| 5 | Classification based on inside vs. on/outside unit circle | 100.00% | 50 of 50 correct |
| 6 | Classification based on exact distance from origin | 100.00% | 50 of 50 correct |
| 7 | Final refined distance-based classification | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.448, -0.894] | 0 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.448, -0.894] | 0 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.448, -0.894] | 0 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 4 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | High y-value with x near zero threshold | 52.00% | 26 of 50 correct |
| 1 | Product of x and y threshold | 64.00% | 32 of 50 correct |
| 2 | Sum of x and y | 52.00% | 26 of 50 correct |
| 3 | Distance from origin (radius) | 100.00% | 50 of 50 correct |
| 4 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 5 | k-Nearest Neighbors with k=5 | 0.00% | 0 of 50 correct |
| 6 | Quadrant-based classification | 58.00% | 29 of 50 correct |
| 7 | Product of x and y sign-based classification | 58.00% | 29 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [-0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [-0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 5 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on angle in third quadrant | 50.00% | 25 of 50 correct |
| 1 | Classification based on magnitude (distance from origin) | 100.00% | 50 of 50 correct |
| 2 | Classification based on sum of coordinates threshold | 62.00% | 31 of 50 correct |
| 3 | Classification based on k-nearest neighbors (k=3) | 0.00% | 0 of 50 correct |
| 4 | Classification based on position relative to line y = x in third quadrant | 46.00% | 23 of 50 correct |
| 5 | Classification based on quadratic term x² - y² | 58.00% | 29 of 50 correct |
| 6 | Classification based on y-coordinate threshold in third quadrant | 46.00% | 23 of 50 correct |
| 7 | Classification based on proximity to axes | 42.00% | 21 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [-0.482, 0.760] | 1 | ERROR | ✗ WRONG |
| [0.899, 0.045] | 1 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 6 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Distance-based classification: points inside unit circle are class 1, points on or outside are class 0 | 94.00% | 47 of 50 correct |
| 1 | Quadrant IV rule: if y < -x then class 1, else class 0 | 46.00% | 23 of 50 correct |
| 2 | Third quadrant rule: if |y| > |x| then class 1, else class 0 | 50.00% | 25 of 50 correct |
| 3 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 4 | k-Nearest Neighbors with k=1 | 0.00% | 0 of 50 correct |
| 5 | k-Nearest Neighbors with k=5 | 0.00% | 0 of 50 correct |
| 6 | Diamond shape boundary: points inside diamond are class 1, outside are class 0 | 58.00% | 29 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 7 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Exact match lookup | 56.00% | 28 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 0 | ✗ WRONG |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 8 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | k-NN classification with k=3 | 0.00% | 0 of 50 correct |
| 1 | Threshold on sum of coordinates | 56.00% | 28 of 50 correct |
| 2 | Threshold on y-coordinate | 48.00% | 24 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [-0.888, 0.460] | 0 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.169, 0.884] | 1 | ERROR | ✗ WRONG |
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| [0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 1 | ✗ WRONG |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 1 | ✗ WRONG |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 9 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 1 | k-Nearest Neighbors with k=1 | 0.00% | 0 of 50 correct |
| 2 | X-value threshold for high y-values | 58.00% | 29 of 50 correct |
| 3 | Linear decision boundary y = -x + 1.2 | 50.00% | 25 of 50 correct |
| 4 | Linear decision boundary y = 2x + 0.5 | 52.00% | 26 of 50 correct |
| 5 | Radius-based classification with threshold at 0.95 | 100.00% | 50 of 50 correct |
| 6 | Final radius-based classification model | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | ERROR | ✗ WRONG |
| [0.746, -0.666] | 0 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | ERROR | ✗ WRONG |
| [0.746, -0.666] | 0 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.671, 0.599] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.671, 0.599] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 1 | ✗ WRONG |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.671, 0.599] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 1 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 10 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Angle-based classification in polar coordinates | 46.00% | 23 of 50 correct |
| 1 | Threshold on x + y in Quadrant IV | 48.00% | 24 of 50 correct |
| 2 | Threshold on x - y in Quadrant IV | 54.00% | 27 of 50 correct |
| 3 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 4 | High x-value threshold in Quadrant IV | 48.00% | 24 of 50 correct |
| 5 | Threshold on y-value in Quadrant I | 58.00% | 29 of 50 correct |
| 6 | Distance-based classification from reference points | 50.00% | 25 of 50 correct |
| 7 | Unit circle distance classification | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 1 | ✗ WRONG |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 1 | ✗ WRONG |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [-0.448, -0.894] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| [0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 1 | ✗ WRONG |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| [0.501, 0.748] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 11 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on y-value threshold | 0.00% | 0 of 50 correct |
| 1 | Classification based on x-value and y-value combination | 52.00% | 26 of 50 correct |
| 2 | Classification based on quadrant position | 52.00% | 26 of 50 correct |
| 3 | Classification based on x-value threshold in third quadrant | 50.00% | 25 of 50 correct |
| 4 | Classification based on sum of features | 0.00% | 0 of 50 correct |
| 5 | Classification based on ratio of x to y | 0.00% | 0 of 50 correct |
| 6 | k-Nearest Neighbors classification with k=5 | 0.00% | 0 of 50 correct |
| 7 | k-Nearest Neighbors classification with k=3 | 0.00% | 0 of 50 correct |
| 8 | k-Nearest Neighbors classification with k=1 | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 12 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Y-coordinate threshold rule | 54.00% | 27 of 50 correct |
| 1 | k-Nearest Neighbors classification (k=3) | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 1 | ✗ WRONG |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [-0.640, -0.632] | 1 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 13 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 44.00% | 22 of 50 correct |
| 1 | Comparison of y and absolute value of x | 52.00% | 26 of 50 correct |
| 2 | Product of features threshold | 52.00% | 26 of 50 correct |
| 3 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 4 | Boundary based on x and y thresholds | 54.00% | 27 of 50 correct |
| 5 | Boundary based on x and y thresholds (refined) | 54.00% | 27 of 50 correct |
| 6 | Unit circle classification | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| [-0.463, 0.772] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 14 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 52.00% | 26 of 50 correct |
| 1 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 2 | Y-threshold classification | 60.00% | 30 of 50 correct |
| 3 | Distance from origin classification (Quadrant III) | 58.00% | 29 of 50 correct |
| 4 | Unit circle classification | 94.00% | 47 of 50 correct |
| 5 | Modified unit circle with exceptions | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [-0.640, -0.632] | 1 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 1 | ✗ WRONG |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 15 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Second quadrant boundary at x = -0.9 | 46.00% | 23 of 50 correct |
| 1 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 2 | Proximity to edge of second quadrant | 44.00% | 22 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| [-0.992, 0.125] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [-0.309, -0.951] | 0 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 16 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 66.00% | 33 of 50 correct |
| 1 | Sum threshold classification | 66.00% | 33 of 50 correct |
| 2 | Distance from origin classification | 100.00% | 50 of 50 correct |
| 3 | Circle boundary classification | 70.00% | 35 of 50 correct |
| 4 | Line boundary y = -x classification | 38.00% | 19 of 50 correct |
| 5 | Fourth quadrant with magnitude comparison | 48.00% | 24 of 50 correct |
| 6 | Threshold-based classification | 54.00% | 27 of 50 correct |
| 7 | Vertical line with quadrant classification | 62.00% | 31 of 50 correct |
| 8 | High x-value with positive y classification | 58.00% | 29 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 1 | ✗ WRONG |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 17 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Proximity to [0.985, 0.175] suggests class 0 | 54.00% | 27 of 50 correct |
| 1 | High first feature with low second feature is class 0 | 54.00% | 27 of 50 correct |
| 2 | Points in extreme ends of quadrants are class 0 | 58.00% | 29 of 50 correct |
| 3 | Circular boundary based on sum of squares | 100.00% | 50 of 50 correct |
| 4 | Points on unit circle are class 0, inside are class 1 | 100.00% | 50 of 50 correct |
| 5 | Final rule: points on unit circle are class 0, others are class 1 | 100.00% | 50 of 50 correct |
| 6 | Validation of the unit circle rule | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 0 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 0 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 0 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 18 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | k-NN with k=1 (nearest neighbor classification) | 0.00% | 0 of 50 correct |
| 1 | Linear boundary based on x1 and x2 values | 0.00% | 0 of 50 correct |
| 2 | k-NN with k=3 (majority voting) | 0.00% | 0 of 50 correct |
| 3 | Threshold on x1 value | 66.00% | 33 of 50 correct |
| 4 | Product of x1 and x2 threshold | 48.00% | 24 of 50 correct |
| 5 | Distance from origin (unit circle boundary) | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| [0.187, -0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 19 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on angle ranges | 40.00% | 20 of 50 correct |
| 1 | K-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 2 | Classification based on y-value threshold | 60.00% | 30 of 50 correct |
| 3 | Classification based on y-value threshold of 0.9 | 48.00% | 24 of 50 correct |
| 4 | Classification based on product of x and y | 50.00% | 25 of 50 correct |
| 5 | Classification based on line y = x | 46.00% | 23 of 50 correct |
| 6 | Classification based on line y = 1 - x | 36.00% | 18 of 50 correct |
| 7 | Classification based on x threshold | 58.00% | 29 of 50 correct |
| 8 | Classification based on proximity to axes or corners | 74.00% | 37 of 50 correct |
| 9 | Classification based on combined x and y thresholds | 46.00% | 23 of 50 correct |
| 10 | K-Nearest Neighbors with k=1 | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 20 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 42.00% | 21 of 50 correct |
| 1 | Feature comparison (x2 > x1) | 56.00% | 28 of 50 correct |
| 2 | Sum of features | 0.00% | 0 of 50 correct |
| 3 | Angle in polar coordinates | 0.00% | 0 of 50 correct |
| 4 | Distance from origin (radius) | 100.00% | 50 of 50 correct |
| 5 | Circle boundary (points on unit circle vs inside) | 100.00% | 50 of 50 correct |
| 6 | Final radius-based classification | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [-0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 0 | ✗ WRONG |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| [-0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| [-0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 21 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 42.00% | 21 of 50 correct |
| 1 | Line y = -x as decision boundary | 60.00% | 30 of 50 correct |
| 2 | Sum of features (x + y) as decision boundary | 40.00% | 20 of 50 correct |
| 3 | Product of features (x * y) as decision boundary | 52.00% | 26 of 50 correct |
| 4 | k-nearest neighbors classification | 0.00% | 0 of 50 correct |
| 5 | Distance from origin (unit circle) as decision boundary | 94.00% | 47 of 50 correct |
| 6 | Final validation of unit circle model | 94.00% | 47 of 50 correct |
| 7 | Final classification of target point using unit circle model | 94.00% | 47 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [0.897, 0.068] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.102, -0.894] | 1 | ERROR | ✗ WRONG |
| [-0.675, -0.738] | 0 | ERROR | ✗ WRONG |
| [0.899, 0.045] | 1 | ERROR | ✗ WRONG |
| [-0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [0.897, 0.068] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 22 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Circle with radius ~0.9 decision boundary | 70.00% | 35 of 50 correct |
| 1 | Circle with radius 1.0 decision boundary | 98.00% | 49 of 50 correct |
| 2 | High y-value decision boundary | 58.00% | 29 of 50 correct |
| 3 | High x-value decision boundary | 60.00% | 30 of 50 correct |
| 4 | Combined high x-value and circle decision boundary | 98.00% | 49 of 50 correct |
| 5 | High x-value with y-value constraint | 60.00% | 30 of 50 correct |
| 6 | Union of high x-value and circle decision boundary | 98.00% | 49 of 50 correct |
| 7 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 1 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [0.557, -0.831] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [-0.989, 0.150] | 0 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 23 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 66.00% | 33 of 50 correct |
| 1 | Angle-based classification | 60.00% | 30 of 50 correct |
| 2 | Upper/lower half-circle classification | 68.00% | 34 of 50 correct |
| 3 | Sum of coordinates threshold | 54.00% | 27 of 50 correct |
| 4 | Difference of coordinates threshold | 64.00% | 32 of 50 correct |
| 5 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 6 | Linear boundary where x > y → label 0, else label 1 | 64.00% | 32 of 50 correct |
| 7 | Threshold on x or y coordinates | 56.00% | 28 of 50 correct |
| 8 | Ratio of y/x threshold | 56.00% | 28 of 50 correct |
| 9 | Combined ratio and x-value threshold | 60.00% | 30 of 50 correct |
| 10 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 11 | k-Nearest Neighbors with k=5 | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 24 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 34.00% | 17 of 50 correct |
| 1 | Magnitude of x in lower left quadrant | 40.00% | 20 of 50 correct |
| 2 | Ratio of x and y magnitudes | 44.00% | 22 of 50 correct |
| 3 | Distance from origin threshold | 96.00% | 48 of 50 correct |
| 4 | Circle boundary decision rule | 96.00% | 48 of 50 correct |
| 5 | Final distance-based classification with inclusive threshold | 96.00% | 48 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 25 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on angle in polar coordinates | 58.00% | 29 of 50 correct |
| 1 | Classification based on distance from origin (unit circle) | 100.00% | 50 of 50 correct |
| 2 | Refined unit circle classification model | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.989, 0.150] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 26 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 46.00% | 23 of 50 correct |
| 1 | Angle-based classification | 0.00% | 0 of 50 correct |
| 2 | Unit circle classification (points on/outside circle are 0, inside are 1) | 92.00% | 46 of 50 correct |
| 3 | Points very close to unit circle are 0, others are 1 | 0.00% | 0 of 50 correct |
| 4 | Comparison of x and y values | 40.00% | 20 of 50 correct |
| 5 | Product of x and y | 0.00% | 0 of 50 correct |
| 6 | Sign of coordinates | 36.00% | 18 of 50 correct |
| 7 | Sign of product x*y | 52.00% | 26 of 50 correct |
| 8 | Sum of x and y | 0.00% | 0 of 50 correct |
| 9 | Circle with radius 0.95 (points inside are 1, outside are 0) | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.448, -0.894] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.493, -0.870] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [-0.448, -0.894] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.493, -0.870] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [-0.448, -0.894] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | 0 | ✗ WRONG |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.448, -0.894] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.493, -0.870] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [-0.448, -0.894] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | 0 | ✗ WRONG |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| [-0.063, 0.998] | 0 | 1 | ✗ WRONG |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [-0.448, -0.894] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [-0.493, -0.870] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [-0.448, -0.894] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 27 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on signs of features | 40.00% | 20 of 50 correct |
| 1 | Classification based on product or sum of features | 54.00% | 27 of 50 correct |
| 2 | Classification based on angle from origin | 54.00% | 27 of 50 correct |
| 3 | Classification based on feature magnitudes | 0.00% | 0 of 50 correct |
| 4 | Unit circle classification: points on unit circle are class 0, points inside are class 1 | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [0.893, 0.113] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [0.877, -0.202] | 1 | 0 | ✗ WRONG |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [0.893, 0.113] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [0.877, -0.202] | 1 | 0 | ✗ WRONG |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [0.893, 0.113] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | ERROR | ✗ WRONG |
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 28 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrant (x1>0, x2>0) | 60.00% | 30 of 50 correct |
| 1 | Classification based on product of coordinates (x1*x2) | 46.00% | 23 of 50 correct |
| 2 | Classification based on sum of coordinates (x1+x2) | 40.00% | 20 of 50 correct |
| 3 | Classification based on x2 threshold in second quadrant | 54.00% | 27 of 50 correct |
| 4 | Classification based on x2 > -x1 relationship | 60.00% | 30 of 50 correct |
| 5 | Classification based on distance from origin (unit circle) | 100.00% | 50 of 50 correct |
| 6 | Validation of unit circle classification for points inside the circle | 100.00% | 50 of 50 correct |
| 7 | Further validation of unit circle classification model | 100.00% | 50 of 50 correct |
| 8 | Application of unit circle model to test point | 100.00% | 50 of 50 correct |
| 9 | Final verification of unit circle model with additional examples | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 0 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [0.169, 0.884] | 1 | 0 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| [0.671, 0.599] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 29 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 54.00% | 27 of 50 correct |
| 1 | Product of features | 54.00% | 27 of 50 correct |
| 2 | Distance from origin (magnitude) | 100.00% | 50 of 50 correct |
| 3 | Angle-based classification in second quadrant | 60.00% | 30 of 50 correct |
| 4 | Second quadrant points with specific x and y thresholds | 58.00% | 29 of 50 correct |
| 5 | Second quadrant with higher y threshold | 54.00% | 27 of 50 correct |
| 6 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 7 | Linear separator in second quadrant | 52.00% | 26 of 50 correct |
| 8 | Steeper linear separator in second quadrant | 52.00% | 26 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.618, -0.786] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [-0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.300, 0.849] | 1 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.618, -0.786] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 30 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 46.00% | 23 of 50 correct |
| 1 | Sum or product of features | 46.00% | 23 of 50 correct |
| 2 | Magnitude-based classification (distance from origin) | 100.00% | 50 of 50 correct |
| 3 | Circle with radius between 0.9 and 1.0 | 100.00% | 50 of 50 correct |
| 4 | Sum of squares threshold at 0.81 | 94.00% | 47 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 31 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on sign combinations of features | 48.00% | 24 of 50 correct |
| 1 | Classification based on sum of features | 40.00% | 20 of 50 correct |
| 2 | Classification based on product of features | 50.00% | 25 of 50 correct |
| 3 | Classification based on quadrants | 42.00% | 21 of 50 correct |
| 4 | Classification based on distance from origin (unit circle) | 100.00% | 50 of 50 correct |
| 5 | Refined classification based on distance from origin | 100.00% | 50 of 50 correct |
| 6 | Final classification based on distance from origin | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 32 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | k-Nearest Neighbors with k=1 | 0.00% | 0 of 50 correct |
| 1 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 2 | Distance from origin (unit circle boundary) | 100.00% | 50 of 50 correct |
| 3 | Refined distance from origin (unit circle boundary) | 56.00% | 28 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.404, 0.804] | 1 | 0 | ✗ WRONG |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 33 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 0.00% | 0 of 50 correct |
| 1 | Classification based on angle thresholds | 48.00% | 24 of 50 correct |
| 2 | Classification based on distance from origin (unit circle) | 100.00% | 50 of 50 correct |
| 3 | Refined unit circle classification | 100.00% | 50 of 50 correct |
| 4 | Final validation of unit circle model | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [-0.448, -0.894] | 0 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.448, -0.894] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 34 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | High positive x and y sign rule | 56.00% | 28 of 50 correct |
| 1 | Y threshold for high x values | 58.00% | 29 of 50 correct |
| 2 | Sum of coordinates threshold | 58.00% | 29 of 50 correct |
| 3 | Difference of coordinates threshold | 60.00% | 30 of 50 correct |
| 4 | Diagonal line boundary | 52.00% | 26 of 50 correct |
| 5 | Nearest neighbor classification | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.656, -0.616] | 1 | 0 | ✗ WRONG |
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.656, -0.616] | 1 | 0 | ✗ WRONG |
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.656, -0.616] | 1 | 0 | ✗ WRONG |
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [-0.618, -0.786] | 0 | ERROR | ✗ WRONG |
| [-0.309, -0.951] | 0 | ERROR | ✗ WRONG |
| [-0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [-0.656, -0.616] | 1 | ERROR | ✗ WRONG |
| [-0.888, 0.460] | 0 | ERROR | ✗ WRONG |
| [-0.463, 0.772] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 35 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 52.00% | 26 of 50 correct |
| 1 | Sum of features threshold | 0.00% | 0 of 50 correct |
| 2 | Product of features sign | 50.00% | 25 of 50 correct |
| 3 | Sign-based classification | 52.00% | 26 of 50 correct |
| 4 | Angle in polar coordinates | 0.00% | 0 of 50 correct |
| 5 | First quadrant split by y = x | 68.00% | 34 of 50 correct |
| 6 | Ratio-based classification | 54.00% | 27 of 50 correct |
| 7 | Unit circle boundary | 96.00% | 48 of 50 correct |
| 8 | Exact unit circle boundary | 100.00% | 50 of 50 correct |
| 9 | Inside unit circle classification | 96.00% | 48 of 50 correct |
| 10 | Outside unit circle classification | 96.00% | 48 of 50 correct |
| 11 | Linear separator | 0.00% | 0 of 50 correct |
| 12 | Diagonal line separator | 46.00% | 23 of 50 correct |
| 13 | Threshold-based classification | 60.00% | 30 of 50 correct |
| 14 | Circle with radius 0.9 | 76.00% | 38 of 50 correct |
| 15 | Circle with radius 0.9 (exact threshold) | 98.00% | 49 of 50 correct |
| 16 | Angle-based classification | 0.00% | 0 of 50 correct |
| 17 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [-0.851, -0.525] | 0 | 1 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 36 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Exact match lookup | 40.00% | 20 of 50 correct |
| 1 | Region-based classification | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | 0 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| [-0.191, 0.880] | 1 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 37 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 42.00% | 21 of 50 correct |
| 1 | X-coordinate threshold in fourth quadrant | 42.00% | 21 of 50 correct |
| 2 | Sum of coordinates (x + y) classification | 62.00% | 31 of 50 correct |
| 3 | Distance from origin (magnitude) classification | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 38 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 66.00% | 33 of 50 correct |
| 1 | X/Y ratio threshold classification | 50.00% | 25 of 50 correct |
| 2 | Distance from origin (unit circle) classification | 94.00% | 47 of 50 correct |
| 3 | Angle-based classification | 48.00% | 24 of 50 correct |
| 4 | Feature product classification | 44.00% | 22 of 50 correct |
| 5 | Feature difference classification | 40.00% | 20 of 50 correct |
| 6 | Final unit circle classification | 94.00% | 47 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 0 | ✗ WRONG |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 39 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 1 | Classification based on sum of coordinates | 56.00% | 28 of 50 correct |
| 2 | Classification based on product of coordinates | 50.00% | 25 of 50 correct |
| 3 | Classification based on quadrant | 56.00% | 28 of 50 correct |
| 4 | Classification based on distance from origin | 100.00% | 50 of 50 correct |
| 5 | Classification based on distance from origin (confirmed) | 100.00% | 50 of 50 correct |
| 6 | Final validation of distance-based classification | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [-0.618, -0.786] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.989, 0.150] | 0 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 40 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Unit circle boundary: distance < 1 → Class 1, distance ≥ 1 → Class 0 | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 41 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 44.00% | 22 of 50 correct |
| 1 | Classification based on distance from origin (unit circle) | 100.00% | 50 of 50 correct |
| 2 | Classification based on specific magnitude thresholds | 100.00% | 50 of 50 correct |
| 3 | Classification based on radius threshold around 0.95 | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 1 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 42 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 64.00% | 32 of 50 correct |
| 1 | Distance from origin threshold | 0.00% | 0 of 50 correct |
| 2 | Angle-based classification | 0.00% | 0 of 50 correct |
| 3 | X-value threshold | 56.00% | 28 of 50 correct |
| 4 | Y-value sign classification | 62.00% | 31 of 50 correct |
| 5 | Diagonal line classification | 62.00% | 31 of 50 correct |
| 6 | Unit circle classification | 96.00% | 48 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [-0.992, 0.125] | 0 | ERROR | ✗ WRONG |
| [0.557, -0.831] | 0 | ERROR | ✗ WRONG |
| [-0.463, 0.772] | 1 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.897, 0.068] | 1 | ERROR | ✗ WRONG |
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [-0.992, 0.125] | 0 | ERROR | ✗ WRONG |
| [0.557, -0.831] | 0 | ERROR | ✗ WRONG |
| [-0.463, 0.772] | 1 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.897, 0.068] | 1 | ERROR | ✗ WRONG |
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.992, 0.125] | 0 | 1 | ✗ WRONG |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 43 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 50.00% | 25 of 50 correct |
| 1 | Magnitude comparison (x > |y|) | 54.00% | 27 of 50 correct |
| 2 | Sum threshold (x + y > threshold) | 60.00% | 30 of 50 correct |
| 3 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 4 | Distance from origin classification (circle-based) | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [0.169, 0.884] | 1 | 0 | ✗ WRONG |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [0.169, 0.884] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 44 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | High x-value threshold model | 56.00% | 28 of 50 correct |
| 1 | High x and low y threshold model | 56.00% | 28 of 50 correct |
| 2 | Angle from origin model | 58.00% | 29 of 50 correct |
| 3 | Distance squared from origin model | 82.00% | 41 of 50 correct |
| 4 | Product of features threshold model | 56.00% | 28 of 50 correct |
| 5 | Absolute product threshold model | 58.00% | 29 of 50 correct |
| 6 | k-Nearest Neighbors model | 0.00% | 0 of 50 correct |
| 7 | High x and low y threshold model (revisited) | 56.00% | 28 of 50 correct |
| 8 | Circle boundary model | 98.00% | 49 of 50 correct |
| 9 | Final circle boundary model | 98.00% | 49 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [-0.493, -0.870] | 0 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.482, 0.760] | 1 | ERROR | ✗ WRONG |
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [-0.463, 0.772] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 45 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 50.00% | 25 of 50 correct |
| 1 | Classification based on sign of product of features | 50.00% | 25 of 50 correct |
| 2 | Classification based on sum of features | 50.00% | 25 of 50 correct |
| 3 | Classification based on distance from origin | 100.00% | 50 of 50 correct |
| 4 | Classification based on polar angle | 0.00% | 0 of 50 correct |
| 5 | Classification based on x threshold | 52.00% | 26 of 50 correct |
| 6 | Classification based on y threshold | 48.00% | 24 of 50 correct |
| 7 | Classification based on x+y threshold | 50.00% | 25 of 50 correct |
| 8 | Classification based on squared distance from origin (x²+y²) | 98.00% | 49 of 50 correct |
| 9 | Classification based on distance from origin with radius 0.95 | 100.00% | 50 of 50 correct |
| 10 | Final classification based on squared distance from origin with threshold 0.81 | 98.00% | 49 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [0.746, -0.666] | 0 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [-0.191, 0.880] | 1 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 46 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Product of features determines class | 54.00% | 27 of 50 correct |
| 1 | Sum of features determines class | 56.00% | 28 of 50 correct |
| 2 | Distance from origin determines class (distance < 0.9 → Class 1) | 56.00% | 28 of 50 correct |
| 3 | Distance from origin determines class (distance < 1.0 → Class 1) | 100.00% | 50 of 50 correct |
| 4 | Distance from origin determines class (distance < 0.95 → Class 1) | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 47 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 72.00% | 36 of 50 correct |
| 1 | Classification based on product of features | 54.00% | 27 of 50 correct |
| 2 | Classification based on distance from origin | 56.00% | 28 of 50 correct |
| 3 | Classification based on x and y signs | 68.00% | 34 of 50 correct |
| 4 | Classification based on y threshold | 54.00% | 27 of 50 correct |
| 5 | Classification based on x/y ratio | 58.00% | 29 of 50 correct |
| 6 | Classification based on unit circle boundary | 94.00% | 47 of 50 correct |
| 7 | Final validation of unit circle model | 94.00% | 47 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.169, 0.884] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.169, 0.884] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 48 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | High x and y values determine class | 0.00% | 0 of 50 correct |
| 1 | Quadrant-based classification | 52.00% | 26 of 50 correct |
| 2 | Linear decision boundary | 64.00% | 32 of 50 correct |
| 3 | Ratio of y to x determines class | 48.00% | 24 of 50 correct |
| 4 | Unit circle boundary (x² + y² = 1) | 96.00% | 48 of 50 correct |
| 5 | Refined unit circle boundary with threshold 0.999 | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.191, 0.880] | 1 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 49 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Distance from origin determines class: points on unit circle (magnitude ~1) are class 0, points inside (magnitude ~0.9) are class 1 | 100.00% | 50 of 50 correct |
| 1 | Circle with radius around 0.95 as decision boundary | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 50 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Positive x and y patterns | 62.00% | 31 of 50 correct |
| 1 | High x and positive y threshold | 56.00% | 28 of 50 correct |
| 2 | Ratio of y to x | 0.00% | 0 of 50 correct |
| 3 | Low x and high negative y | 24.00% | 12 of 50 correct |
| 4 | Distance from origin threshold | 100.00% | 50 of 50 correct |
| 5 | Unit circle boundary | 100.00% | 50 of 50 correct |
| 6 | Final unit circle boundary validation | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.851, -0.525] | 0 | 1 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.851, -0.525] | 0 | ERROR | ✗ WRONG |
| [-0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.675, -0.738] | 0 | ERROR | ✗ WRONG |
| [-0.482, 0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 51 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 64.00% | 32 of 50 correct |
| 1 | Classification based on sum of features | 0.00% | 0 of 50 correct |
| 2 | Classification based on product of features | 0.00% | 0 of 50 correct |
| 3 | Classification based on distance from origin (radius 0.9) | 96.00% | 48 of 50 correct |
| 4 | Classification based on unit circle boundary | 96.00% | 48 of 50 correct |
| 5 | Final verification of the unit circle boundary rule | 96.00% | 48 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [-0.640, -0.632] | 1 | ERROR | ✗ WRONG |
| [-0.463, 0.772] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [-0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [-0.640, -0.632] | 1 | ERROR | ✗ WRONG |
| [-0.463, 0.772] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 52 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Positive feature values → Class 1 | 66.00% | 33 of 50 correct |
| 1 | Higher x-value → Class 1 | 64.00% | 32 of 50 correct |
| 2 | Ratio of x/y determines class | 46.00% | 23 of 50 correct |
| 3 | Sum of features determines class | 46.00% | 23 of 50 correct |
| 4 | Distance from origin determines class | 100.00% | 50 of 50 correct |
| 5 | Angle from x-axis determines class | 54.00% | 27 of 50 correct |
| 6 | High y-value when x is positive determines class | 50.00% | 25 of 50 correct |
| 7 | Unit circle boundary: inside → Class 1, on circle → Class 0 | 100.00% | 50 of 50 correct |
| 8 | Final validation of unit circle model | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 0 | ✗ WRONG |
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.897, 0.068] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 0 | ✗ WRONG |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 53 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 64.00% | 32 of 50 correct |
| 1 | Classification based on second feature's sign | 62.00% | 31 of 50 correct |
| 2 | Classification based on sum of features | 62.00% | 31 of 50 correct |
| 3 | Classification based on linear separation | 56.00% | 28 of 50 correct |
| 4 | Classification based on angle from origin | 58.00% | 29 of 50 correct |
| 5 | Classification based on product of x and y | 52.00% | 26 of 50 correct |
| 6 | Classification based on magnitude of x | 44.00% | 22 of 50 correct |
| 7 | Classification based on distance from a point | 96.00% | 48 of 50 correct |
| 8 | Classification based on line in quadrant II | 52.00% | 26 of 50 correct |
| 9 | Classification based on y > -x | 52.00% | 26 of 50 correct |
| 10 | Classification based on distance to unit circle | 100.00% | 50 of 50 correct |
| 11 | Classification based on nearest neighbors | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 0 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 0 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| [0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [-0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| [-0.640, -0.632] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 54 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Circular decision boundary based on distance from origin | 100.00% | 50 of 50 correct |
| 1 | Decision boundary based on y/x ratio | 42.00% | 21 of 50 correct |
| 2 | Line boundary x + y = -1 | 56.00% | 28 of 50 correct |
| 3 | Rule based on x and y thresholds | 50.00% | 25 of 50 correct |
| 4 | Vertical line boundary at x=-0.9 | 50.00% | 25 of 50 correct |
| 5 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 0 | ✗ WRONG |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 55 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Unit circle decision boundary | 96.00% | 48 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 56 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrant (fourth quadrant) | 56.00% | 28 of 50 correct |
| 1 | Classification based on distance from origin (radius) | 100.00% | 50 of 50 correct |
| 2 | Classification based on y-value threshold | 62.00% | 31 of 50 correct |
| 3 | Classification based on linear boundary y = -x + c | 64.00% | 32 of 50 correct |
| 4 | Classification based on diagonal line with specific slope | 60.00% | 30 of 50 correct |
| 5 | Classification based on distance from origin with specific threshold | 100.00% | 50 of 50 correct |
| 6 | Final validation of the radius-based classification | 100.00% | 50 of 50 correct |
| 7 | Final confirmation of the radius-based classification rule | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [-0.578, -0.816] | 0 | 1 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 57 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | k-Nearest Neighbors (k=2) | 0.00% | 0 of 50 correct |
| 1 | k-Nearest Neighbors (k=3) | 0.00% | 0 of 50 correct |
| 2 | Unit Circle Decision Boundary | 94.00% | 47 of 50 correct |
| 3 | Final Unit Circle Decision Boundary | 94.00% | 47 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 58 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Distance-based classification: points on unit circle are class 0, points inside are class 1 | 94.00% | 47 of 50 correct |
| 1 | Refined distance-based classification with exact threshold | 94.00% | 47 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 59 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on product of features | 48.00% | 24 of 50 correct |
| 1 | Classification based on distance from origin (unit circle) | 100.00% | 50 of 50 correct |
| 2 | Classification based on unit circle (refined) | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 60 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 54.00% | 27 of 50 correct |
| 1 | Second quadrant classification | 58.00% | 29 of 50 correct |
| 2 | Distance from origin classification (radius ≈ 0.9 → Class 1, radius ≈ 1.0 → Class 0) | 100.00% | 50 of 50 correct |
| 3 | Distance threshold classification (distance < 1.0 → Class 1, distance ≥ 1.0 → Class 0) | 92.00% | 46 of 50 correct |
| 4 | Linear combination of features | 0.00% | 0 of 50 correct |
| 5 | Linear combination with difference of features | 0.00% | 0 of 50 correct |
| 6 | Product of features | 42.00% | 21 of 50 correct |
| 7 | High absolute value classification | 66.00% | 33 of 50 correct |
| 8 | Complex equation with product term | 0.00% | 0 of 50 correct |
| 9 | High y-value with positive x classification | 52.00% | 26 of 50 correct |
| 10 | K-Nearest Neighbors (K=1) | 0.00% | 0 of 50 correct |
| 11 | K-Nearest Neighbors (K=3) | 0.00% | 0 of 50 correct |
| 12 | K-Nearest Neighbors (K=5) | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [0.859, -0.267] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.819, 0.373] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 61 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 68.00% | 34 of 50 correct |
| 1 | Linear boundary based on sum of coordinates | 68.00% | 34 of 50 correct |
| 2 | Circle-based classification with radius 0.95 | 100.00% | 50 of 50 correct |
| 3 | Unit circle classification | 94.00% | 47 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 62 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Distance-based classification with circle boundary | 96.00% | 48 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 63 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 60.00% | 30 of 50 correct |
| 1 | Distance-based classification (norm ≈ 0.9 → class 1, norm ≈ 1.0 → class 0) | 100.00% | 50 of 50 correct |
| 2 | Circle-based classification (inside circle → class 1, on circumference → class 0) | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 0 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.851, -0.525] | 0 | 0 | ✓ CORRECT |
| [-0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 64 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 48.00% | 24 of 50 correct |
| 1 | Classification based on product of features | 50.00% | 25 of 50 correct |
| 2 | Classification based on x-coordinate sign | 52.00% | 26 of 50 correct |
| 3 | Classification based on quadrant I (positive x, negative y) | 46.00% | 23 of 50 correct |
| 4 | Classification based on magnitude comparison of coordinates | 56.00% | 28 of 50 correct |
| 5 | Classification based on distance from origin (unit circle) | 98.00% | 49 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [0.356, 0.934] | 0 | 1 | ✗ WRONG |
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 1 | ✗ WRONG |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 0 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 65 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrant (sign of x and y) | 62.00% | 31 of 50 correct |
| 1 | Classification based on distance from origin (unit circle) | 100.00% | 50 of 50 correct |
| 2 | Classification based on angle ranges | 0.00% | 0 of 50 correct |
| 3 | Classification based on x+y value | 48.00% | 24 of 50 correct |
| 4 | Classification based on y > -0.5x line | 44.00% | 22 of 50 correct |
| 5 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 1 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.363, 0.824] | 1 | 0 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [0.363, 0.824] | 1 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 66 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 52.00% | 26 of 50 correct |
| 1 | Comparison of x and |y| magnitudes | 60.00% | 30 of 50 correct |
| 2 | Line y = -x as decision boundary | 54.00% | 27 of 50 correct |
| 3 | Product of features as decision boundary | 52.00% | 26 of 50 correct |
| 4 | Distance from origin as decision boundary | 96.00% | 48 of 50 correct |
| 5 | Vertical line x = 0.5 as decision boundary | 50.00% | 25 of 50 correct |
| 6 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 7 | k-Nearest Neighbors with k=1 | 0.00% | 0 of 50 correct |
| 8 | Angle from origin as decision boundary | 54.00% | 27 of 50 correct |
| 9 | Ratio of y to x as decision boundary | 52.00% | 26 of 50 correct |
| 10 | Sum of squares as decision boundary | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 1 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.356, -0.934] | 0 | 1 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 1 | ✗ WRONG |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [0.102, -0.894] | 1 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| [0.746, -0.666] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [-0.309, -0.951] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [0.102, -0.894] | 1 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| [0.746, -0.666] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [-0.309, -0.951] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.363, -0.824] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 67 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Direct lookup in training data | 48.00% | 24 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 0 | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 68 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 48.00% | 24 of 50 correct |
| 1 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 2 | k-Nearest Neighbors with k=5 | 0.00% | 0 of 50 correct |
| 3 | Distance from origin threshold | 96.00% | 48 of 50 correct |
| 4 | X-coordinate threshold | 50.00% | 25 of 50 correct |
| 5 | Y-coordinate threshold | 54.00% | 27 of 50 correct |
| 6 | X-coordinate threshold (refined) | 52.00% | 26 of 50 correct |
| 7 | k-Nearest Neighbors with k=1 | 0.00% | 0 of 50 correct |
| 8 | X-coordinate threshold (alternative) | 54.00% | 27 of 50 correct |
| 9 | Y-coordinate threshold (refined) | 54.00% | 27 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.536, -0.844] | 0 | 1 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 69 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on angle in polar coordinates | 0.00% | 0 of 50 correct |
| 1 | Classification based on x and y ratio | 0.00% | 0 of 50 correct |
| 2 | Unit circle classification: points on unit circle are class 0, points inside are class 1 | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [-0.618, -0.786] | 0 | ERROR | ✗ WRONG |
| [-0.191, 0.880] | 1 | ERROR | ✗ WRONG |
| [-0.656, -0.616] | 1 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.578, -0.816] | 0 | ERROR | ✗ WRONG |
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| [-0.618, -0.786] | 0 | ERROR | ✗ WRONG |
| [-0.191, 0.880] | 1 | ERROR | ✗ WRONG |
| [-0.656, -0.616] | 1 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 70 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 48.00% | 24 of 50 correct |
| 1 | Distance from origin | 100.00% | 50 of 50 correct |
| 2 | Y-threshold for negative x values | 58.00% | 29 of 50 correct |
| 3 | Sum of x and y | 44.00% | 22 of 50 correct |
| 4 | Product of x and y | 54.00% | 27 of 50 correct |
| 5 | Angle from origin | 64.00% | 32 of 50 correct |
| 6 | K-nearest neighbors | 0.00% | 0 of 50 correct |
| 7 | Slope from origin threshold | 48.00% | 24 of 50 correct |
| 8 | X-value threshold | 56.00% | 28 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [-0.992, 0.125] | 0 | 1 | ✗ WRONG |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| [-0.992, 0.125] | 0 | ERROR | ✗ WRONG |
| [-0.640, -0.632] | 1 | ERROR | ✗ WRONG |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| [-0.712, 0.703] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 0 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| [-0.712, 0.703] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 71 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | k-Nearest Neighbors (k=3) | 0.00% | 0 of 50 correct |
| 1 | k-Nearest Neighbors (k=5) | 0.00% | 0 of 50 correct |
| 2 | k-Nearest Neighbors (k=1) | 0.00% | 0 of 50 correct |
| 3 | Distance from unit circle | 100.00% | 50 of 50 correct |
| 4 | Product of coordinates threshold | 64.00% | 32 of 50 correct |
| 5 | Y-coordinate threshold | 60.00% | 30 of 50 correct |
| 6 | Sum of coordinates threshold | 48.00% | 24 of 50 correct |
| 7 | Hyperbola boundary | 62.00% | 31 of 50 correct |
| 8 | Coordinate comparison | 50.00% | 25 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.656, -0.616] | 1 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.656, -0.616] | 1 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.656, -0.616] | 1 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [0.187, 0.982] | 0 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 0 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 1 | ✗ WRONG |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.656, -0.616] | 1 | 0 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [0.187, 0.982] | 0 | 0 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 72 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 18.00% | 9 of 50 correct |
| 1 | Y-value threshold in first quadrant | 22.00% | 11 of 50 correct |
| 2 | Distance from origin classification | 100.00% | 50 of 50 correct |
| 3 | Distance threshold classification | 100.00% | 50 of 50 correct |
| 4 | Final validation of distance-based classification | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.899, 0.045] | 1 | ERROR | ✗ WRONG |
| [-0.309, -0.951] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.557, -0.831] | 0 | ERROR | ✗ WRONG |
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | ERROR | ✗ WRONG |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.536, -0.844] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [-0.309, -0.951] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.536, -0.844] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 73 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Threshold on x-coordinate | 46.00% | 23 of 50 correct |
| 1 | Threshold on sum of x and y | 48.00% | 24 of 50 correct |
| 2 | Threshold on sum of x and y (revised) | 54.00% | 27 of 50 correct |
| 3 | Threshold on y-coordinate | 62.00% | 31 of 50 correct |
| 4 | Threshold on x-coordinate (revised) | 46.00% | 23 of 50 correct |
| 5 | Distance from origin | 96.00% | 48 of 50 correct |
| 6 | Threshold on x and y coordinates | 52.00% | 26 of 50 correct |
| 7 | Threshold on sum of x and y (another revision) | 44.00% | 22 of 50 correct |
| 8 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 9 | k-Nearest Neighbors with k=5 | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.877, -0.202] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 0 | ✗ WRONG |
| [0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.877, -0.202] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.899, 0.045] | 1 | 1 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.877, -0.202] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [-0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.888, 0.460] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.899, 0.045] | 1 | ERROR | ✗ WRONG |
| [0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.482, 0.760] | 1 | ERROR | ✗ WRONG |
| [-0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [-0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.888, 0.460] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.899, 0.045] | 1 | ERROR | ✗ WRONG |
| [0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.482, 0.760] | 1 | ERROR | ✗ WRONG |
| [-0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.877, -0.202] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 74 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 64.00% | 32 of 50 correct |
| 1 | Distance from origin classification | 100.00% | 50 of 50 correct |
| 2 | Sum of features classification | 72.00% | 36 of 50 correct |
| 3 | Difference of features classification | 48.00% | 24 of 50 correct |
| 4 | K-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 5 | Second feature threshold classification | 66.00% | 33 of 50 correct |
| 6 | Decision tree with x2 threshold | 66.00% | 33 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.169, 0.884] | 1 | 0 | ✗ WRONG |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [0.557, -0.831] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| [0.169, 0.884] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 75 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | k-NN classification with k=1 | 0.00% | 0 of 50 correct |
| 1 | Distance from origin classification | 100.00% | 50 of 50 correct |
| 2 | Final distance-based classification rule | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [-0.640, -0.632] | 1 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.063, 0.998] | 0 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.640, -0.632] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.063, 0.998] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 76 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Points with negative second feature (y) are labeled 0 | 64.00% | 32 of 50 correct |
| 1 | First feature (x) determines the label when the second feature (y) is negative | 32.00% | 16 of 50 correct |
| 2 | Diagonal decision boundary | 0.00% | 0 of 50 correct |
| 3 | Product of features determines the label | 0.00% | 0 of 50 correct |
| 4 | Sum of features determines the label | 0.00% | 0 of 50 correct |
| 5 | When x is positive and y is negative, x value determines the label | 32.00% | 16 of 50 correct |
| 6 | Magnitude of feature vector determines the label (circle with radius ~0.9) | 100.00% | 50 of 50 correct |
| 7 | Quadrant-based classification | 64.00% | 32 of 50 correct |
| 8 | Product of x and y determines quadrant-based classification | 52.00% | 26 of 50 correct |
| 9 | Angle from origin determines the label | 0.00% | 0 of 50 correct |
| 10 | Ratio of x to y determines the label | 0.00% | 0 of 50 correct |
| 11 | Distance from a particular line determines the label | 0.00% | 0 of 50 correct |
| 12 | When y is negative, x threshold determines the label | 22.00% | 11 of 50 correct |
| 13 | Sum of squares (x² + y²) determines the label (circle with radius 0.9) | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 1 | ✗ WRONG |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 1 | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.989, 0.150] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.591, 0.679] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.989, 0.150] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.591, 0.679] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.989, 0.150] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.591, 0.679] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 1 | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.989, 0.150] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.591, 0.679] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.989, 0.150] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.591, 0.679] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [-0.989, 0.150] | 0 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.591, 0.679] | 1 | ERROR | ✗ WRONG |
| [-0.640, 0.632] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 0 | ✗ WRONG |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.591, 0.679] | 1 | 0 | ✗ WRONG |
| [-0.640, 0.632] | 1 | 0 | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, 0.884] | 1 | 1 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.591, 0.679] | 1 | 1 | ✓ CORRECT |
| [-0.640, 0.632] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 77 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on negative features | 54.00% | 27 of 50 correct |
| 1 | Classification based on angle from origin | 60.00% | 30 of 50 correct |
| 2 | Classification based on magnitude (distance from origin) | 100.00% | 50 of 50 correct |
| 3 | Classification based on product of features | 50.00% | 25 of 50 correct |
| 4 | Classification based on squared distance from origin | 100.00% | 50 of 50 correct |
| 5 | Classification based on feature ratio | 68.00% | 34 of 50 correct |
| 6 | Classification based on sum of absolute values | 32.00% | 16 of 50 correct |
| 7 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| 8 | Linear boundary in lower left quadrant | 58.00% | 29 of 50 correct |
| 9 | Vertical line boundary with y condition | 66.00% | 33 of 50 correct |
| 10 | Unit circle boundary (final model) | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 0 | ✗ WRONG |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 1 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.671, 0.599] | 1 | ERROR | ✗ WRONG |
| [0.729, -0.685] | 0 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.501, 0.748] | 1 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.864, -0.504] | 0 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [-0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 0 | ✗ WRONG |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 0 | ✗ WRONG |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 0 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.671, 0.599] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 78 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 50.00% | 25 of 50 correct |
| 1 | K-Nearest Neighbors with K=3 | 0.00% | 0 of 50 correct |
| 2 | K-Nearest Neighbors with K=5 | 0.00% | 0 of 50 correct |
| 3 | Angle-based classification | 52.00% | 26 of 50 correct |
| 4 | Distance from origin classification | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 1 | ✗ WRONG |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [-0.675, -0.738] | 0 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.404, 0.804] | 1 | ERROR | ✗ WRONG |
| [-0.656, 0.616] | 1 | ERROR | ✗ WRONG |
| [0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [-0.675, -0.738] | 0 | ERROR | ✗ WRONG |
| [0.285, 0.959] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.169, -0.884] | 1 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 0 | ✗ WRONG |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 1 | ✗ WRONG |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.404, 0.804] | 1 | 1 | ✓ CORRECT |
| [-0.656, 0.616] | 1 | 1 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [0.285, 0.959] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 79 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Exact match lookup | 52.00% | 26 of 50 correct |
| 1 | High first feature with positive second feature | 56.00% | 28 of 50 correct |
| 2 | High first feature with negative second feature | 50.00% | 25 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [0.169, -0.884] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.618, -0.786] | 0 | 1 | ✗ WRONG |
| [-0.992, 0.125] | 0 | 1 | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 80 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 48.00% | 24 of 50 correct |
| 1 | Sum of features threshold | 46.00% | 23 of 50 correct |
| 2 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 3 | k-Nearest Neighbors with k=5 | 0.00% | 0 of 50 correct |
| 4 | Negative x threshold | 54.00% | 27 of 50 correct |
| 5 | Product of features threshold | 58.00% | 29 of 50 correct |
| 6 | Comparison of y with -x | 52.00% | 26 of 50 correct |
| 7 | Modified line boundary y > -x + 0.2 | 48.00% | 24 of 50 correct |
| 8 | Unit circle with angle-based classification | 60.00% | 30 of 50 correct |
| 9 | First quadrant x vs y comparison | 56.00% | 28 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.886, 0.158] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 1 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.992, 0.125] | 0 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| [0.102, -0.894] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [-0.675, 0.738] | 0 | ERROR | ✗ WRONG |
| [-0.992, 0.125] | 0 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| [0.886, 0.158] | 1 | ERROR | ✗ WRONG |
| [0.102, -0.894] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 1 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 1 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 1 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.992, 0.125] | 0 | 1 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 0 | ✗ WRONG |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.102, 0.894] | 1 | 1 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.992, 0.125] | 0 | 0 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.886, 0.158] | 1 | 0 | ✗ WRONG |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 81 |
| split | test |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 82 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 64.00% | 32 of 50 correct |
| 1 | Unit circle boundary (points inside circle are class 1, on/outside are class 0) | 96.00% | 48 of 50 correct |
| 2 | Product of features threshold | 0.00% | 0 of 50 correct |
| 3 | Angle-based classification (y > |x|) | 40.00% | 20 of 50 correct |
| 4 | k-Nearest Neighbors with k=3 | 0.00% | 0 of 50 correct |
| 5 | k-Nearest Neighbors with k=5 | 0.00% | 0 of 50 correct |
| 6 | k-Nearest Neighbors with k=1 | 0.00% | 0 of 50 correct |
| 7 | X-value threshold for high Y values | 52.00% | 26 of 50 correct |
| 8 | X-value threshold of 0.1 | 44.00% | 22 of 50 correct |
| 9 | Sum of features threshold | 0.00% | 0 of 50 correct |
| 10 | Majority rule for points with y > 0.8 | 54.00% | 27 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [0.443, 0.783] | 1 | 0 | ✗ WRONG |
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.443, 0.783] | 1 | ERROR | ✗ WRONG |
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [0.536, 0.844] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.443, 0.783] | 1 | 1 | ✓ CORRECT |
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [0.536, 0.844] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 83 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Distance-based classification using unit circle | 92.00% | 46 of 50 correct |
| 1 | Product of coordinates determines class | 44.00% | 22 of 50 correct |
| 2 | Quadrant-based classification | 0.00% | 0 of 50 correct |
| 3 | Classification based on y-value threshold | 66.00% | 33 of 50 correct |
| 4 | Classification based on x-value threshold | 56.00% | 28 of 50 correct |
| 5 | Classification based on x and y thresholds | 54.00% | 27 of 50 correct |
| 6 | k-Nearest Neighbors classification with k=1 | 0.00% | 0 of 50 correct |
| 7 | k-Nearest Neighbors classification with k=3 | 0.00% | 0 of 50 correct |
| 8 | Product of coordinates threshold | 44.00% | 22 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.300, 0.849] | 1 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [-0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [0.113, -0.994] | 0 | 1 | ✗ WRONG |
| [-0.598, 0.802] | 0 | 1 | ✗ WRONG |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 1 | ✗ WRONG |
| [0.920, 0.391] | 0 | 0 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.300, 0.849] | 1 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [-0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [-0.300, 0.849] | 1 | ERROR | ✗ WRONG |
| [0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.598, 0.802] | 0 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [-0.845, 0.310] | 1 | ERROR | ✗ WRONG |
| [-0.809, -0.588] | 0 | ERROR | ✗ WRONG |
| [-0.694, 0.720] | 0 | ERROR | ✗ WRONG |
| [0.920, 0.391] | 0 | ERROR | ✗ WRONG |
| [-0.876, -0.482] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 0 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.694, 0.720] | 0 | 0 | ✓ CORRECT |
| [0.920, 0.391] | 0 | 1 | ✗ WRONG |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 84 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on distance from origin (radius) | 100.00% | 50 of 50 correct |
| 1 | Classification based on sum of squares (x² + y²) | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.618, -0.786] | 0 | 0 | ✓ CORRECT |
| [-0.363, -0.824] | 1 | 1 | ✓ CORRECT |
| [-0.989, 0.150] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.191, 0.880] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 85 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Unit circle boundary: points inside are class 1, points on or outside are class 0 | 94.00% | 47 of 50 correct |
| 1 | Quadrant-based classification | 54.00% | 27 of 50 correct |
| 2 | Product of coordinates (x*y) determines class | 50.00% | 25 of 50 correct |
| 3 | Angle-based classification using polar coordinates | 52.00% | 26 of 50 correct |
| 4 | Sum of coordinates (x+y) determines class | 52.00% | 26 of 50 correct |
| 5 | Comparing squared coordinates (x² vs y²) | 60.00% | 30 of 50 correct |
| 6 | Comparing absolute coordinates (|x| vs |y|) | 60.00% | 30 of 50 correct |
| 7 | Circle with radius 0.9 boundary | 64.00% | 32 of 50 correct |
| 8 | k-Nearest Neighbors classification | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 1 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 1 | ✗ WRONG |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 1 | ✗ WRONG |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 0 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.356, 0.934] | 0 | 0 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.063, -0.998] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.300, 0.849] | 1 | 1 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 0 | ✗ WRONG |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [0.356, 0.934] | 0 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.063, -0.998] | 0 | ERROR | ✗ WRONG |
| [-0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.300, 0.849] | 1 | ERROR | ✗ WRONG |
| [-0.766, 0.473] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 86 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 56.00% | 28 of 50 correct |
| 1 | Classification based on distance from origin (unit circle) | 100.00% | 50 of 50 correct |
| 2 | Refined distance-based classification | 100.00% | 50 of 50 correct |
| 3 | Final validation of distance-based classification | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.578, -0.816] | 0 | 1 | ✗ WRONG |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.578, -0.816] | 0 | 0 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.501, 0.748] | 1 | 1 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 87 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on feature sign combinations | 60.00% | 30 of 50 correct |
| 1 | Classification based on quadrants | 66.00% | 33 of 50 correct |
| 2 | Classification based on product of features (x*y) | 60.00% | 30 of 50 correct |
| 3 | Classification based on sum of features (x+y) | 44.00% | 22 of 50 correct |
| 4 | Classification based on distance from origin (around 0.9 vs 1.0) | 100.00% | 50 of 50 correct |
| 5 | Classification based on distance from origin (final rule) | 94.00% | 47 of 50 correct |
| 6 | Final validation of the distance-based classification rule | 94.00% | 47 of 50 correct |
| 7 | Final verification with additional examples | 94.00% | 47 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 0 | ✗ WRONG |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 1 | ✗ WRONG |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 0 | ✗ WRONG |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 1 | ✗ WRONG |
| [0.897, 0.068] | 1 | 0 | ✗ WRONG |
| [0.557, -0.831] | 0 | 1 | ✗ WRONG |
| [0.799, -0.414] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.169, -0.884] | 1 | 1 | ✓ CORRECT |
| [-0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.675, 0.738] | 0 | 0 | ✓ CORRECT |
| [-0.463, 0.772] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [0.102, -0.894] | 1 | 1 | ✓ CORRECT |
| [-0.876, -0.482] | 0 | 0 | ✓ CORRECT |
| [0.897, 0.068] | 1 | 1 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 88 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on x-axis sign | 60.00% | 30 of 50 correct |
| 1 | Classification based on x < y relationship | 48.00% | 24 of 50 correct |
| 2 | Classification based on distance from origin | 100.00% | 50 of 50 correct |
| 3 | Classification based on angle from x-axis | 0.00% | 0 of 50 correct |
| 4 | Classification based on product of features | 0.00% | 0 of 50 correct |
| 5 | Classification based on linear boundary | 0.00% | 0 of 50 correct |
| 6 | Classification based on sum of features | 0.00% | 0 of 50 correct |
| 7 | Classification based on ratio of features | 0.00% | 0 of 50 correct |
| 8 | Classification based on distance from origin squared | 98.00% | 49 of 50 correct |
| 9 | Final refined circle boundary model | 98.00% | 49 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 1 | ✗ WRONG |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [-0.038, 0.999] | 0 | 1 | ✗ WRONG |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | ERROR | ✗ WRONG |
| [0.893, 0.113] | 1 | ERROR | ✗ WRONG |
| [-0.038, 0.999] | 0 | ERROR | ✗ WRONG |
| [0.013, -1.000] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.113, -0.994] | 0 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [-0.187, -0.982] | 0 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [-0.038, 0.999] | 0 | 0 | ✓ CORRECT |
| [0.013, -1.000] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.113, -0.994] | 0 | 0 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 89 |
| split | test |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 90 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 56.00% | 28 of 50 correct |
| 1 | Unit circle boundary (inside → class 1, outside/on → class 0) | 96.00% | 48 of 50 correct |
| 2 | Diagonal line boundary in first quadrant (x1 + x2 = 1) | 48.00% | 24 of 50 correct |
| 3 | k-Nearest Neighbors (k=1) | 0.00% | 0 of 50 correct |
| 4 | k-Nearest Neighbors (k=3) | 0.00% | 0 of 50 correct |
| 5 | Decision tree with x2 threshold in first quadrant | 54.00% | 27 of 50 correct |
| 6 | Product of coordinates (x1*x2) | 44.00% | 22 of 50 correct |
| 7 | Quadrant-based classification (first and third quadrants) | 44.00% | 22 of 50 correct |
| 8 | Modified unit circle with radius 0.95 | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 0 | ✗ WRONG |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 1 | ✗ WRONG |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | ERROR | ✗ WRONG |
| [-0.236, 0.972] | 0 | ERROR | ✗ WRONG |
| [0.715, -0.547] | 1 | ERROR | ✗ WRONG |
| [-0.741, 0.511] | 1 | ERROR | ✗ WRONG |
| [-0.088, -0.996] | 0 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| [0.985, 0.175] | 0 | ERROR | ✗ WRONG |
| [0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 1 | ✗ WRONG |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 1 | ✗ WRONG |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 0 | ✗ WRONG |
| [-0.741, 0.511] | 1 | 0 | ✗ WRONG |
| [-0.088, -0.996] | 0 | 1 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 1 | ✗ WRONG |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.882, 0.180] | 1 | 1 | ✓ CORRECT |
| [-0.236, 0.972] | 0 | 0 | ✓ CORRECT |
| [0.715, -0.547] | 1 | 1 | ✓ CORRECT |
| [-0.741, 0.511] | 1 | 1 | ✓ CORRECT |
| [-0.088, -0.996] | 0 | 0 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| [0.985, 0.175] | 0 | 0 | ✓ CORRECT |
| [0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 91 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 34.00% | 17 of 50 correct |
| 1 | Linear decision boundary based on x and y values | 60.00% | 30 of 50 correct |
| 2 | k-NN with k=1 | 0.00% | 0 of 50 correct |
| 3 | k-NN with k=3 | 0.00% | 0 of 50 correct |
| 4 | Circle-based classification using radius squared | 98.00% | 49 of 50 correct |
| 5 | Refined circle-based classification with radius boundary at 0.9 | 98.00% | 49 of 50 correct |
| 6 | Final circle-based classification with inclusive boundary | 98.00% | 49 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 0 | ✗ WRONG |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | 1 | ✗ WRONG |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [-0.778, 0.628] | 0 | 1 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 1 | ✗ WRONG |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.778, 0.628] | 0 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.799, -0.414] | 1 | ERROR | ✗ WRONG |
| [0.890, 0.135] | 1 | ERROR | ✗ WRONG |
| [-0.910, 0.414] | 0 | ERROR | ✗ WRONG |
| [0.859, -0.267] | 1 | ERROR | ✗ WRONG |
| [0.356, -0.934] | 0 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [0.426, -0.905] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.778, 0.628] | 0 | 0 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.799, -0.414] | 1 | 1 | ✓ CORRECT |
| [0.890, 0.135] | 1 | 1 | ✓ CORRECT |
| [-0.910, 0.414] | 0 | 0 | ✓ CORRECT |
| [0.859, -0.267] | 1 | 1 | ✓ CORRECT |
| [0.356, -0.934] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 92 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Classification based on quadrants | 44.00% | 22 of 50 correct |
| 1 | Classification based on distance from origin (unit circle) | 94.00% | 47 of 50 correct |
| 2 | Points on unit circle are label 0, inside are label 1 | 100.00% | 50 of 50 correct |
| 3 | Final unit circle decision boundary | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | 1 | ✗ WRONG |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [0.893, 0.113] | 1 | 0 | ✗ WRONG |
| [0.845, 0.310] | 1 | 0 | ✗ WRONG |
| [-0.809, -0.588] | 0 | 1 | ✗ WRONG |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 1 | ✗ WRONG |
| [-0.187, -0.982] | 0 | 1 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.888, 0.460] | 0 | 0 | ✓ CORRECT |
| [-0.766, 0.473] | 1 | 1 | ✓ CORRECT |
| [-0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [0.893, 0.113] | 1 | 1 | ✓ CORRECT |
| [0.845, 0.310] | 1 | 1 | ✓ CORRECT |
| [-0.809, -0.588] | 0 | 0 | ✓ CORRECT |
| [-0.819, 0.373] | 1 | 1 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.729, -0.685] | 0 | 0 | ✓ CORRECT |
| [-0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 93 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Points on or near the unit circle (magnitude close to 1) are labeled 0, while points inside (magnitude < ~0.95) are labeled 1 | 100.00% | 50 of 50 correct |
| 1 | Points near the unit circle (radius ~1) are labeled 0; otherwise, they're labeled 1 | 100.00% | 50 of 50 correct |
| 2 | Decision boundary is a circle with radius around 0.95, points inside are labeled 1, points outside or on it are labeled 0 | 100.00% | 50 of 50 correct |
| 3 | If Euclidean distance from origin is approximately 1 (>= 0.95), then label 0; else label 1 | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.675, -0.738] | 0 | 0 | ✓ CORRECT |
| [-0.864, -0.504] | 0 | 0 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [-0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.598, 0.802] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.557, -0.831] | 0 | 0 | ✓ CORRECT |
| [0.187, -0.982] | 0 | 0 | ✓ CORRECT |
| [-0.712, 0.703] | 0 | 0 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 94 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Points with high positive first features and positive second features are class 0 | 46.00% | 23 of 50 correct |
| 1 | Points on or near the unit circle (distance ~1) are class 0, points inside are class 1 | 98.00% | 49 of 50 correct |
| 2 | Points with second feature > 0.7 or < -0.7 are class 0, else class 1 | 60.00% | 30 of 50 correct |
| 3 | If second feature > 0.7 or < -0.7, then class 0. Otherwise, if first feature > 0.8, class 0, else class 1 | 52.00% | 26 of 50 correct |
| 4 | If second feature > 0.7 or < -0.7, then class 0. Otherwise, if first feature > 0.9, class 0, else class 1 | 64.00% | 32 of 50 correct |
| 5 | When first feature > 0.8 and second feature is negative, class is 0 | 54.00% | 27 of 50 correct |
| 6 | If distance squared is >= 0.8, class is 0 | 46.00% | 23 of 50 correct |
| 7 | Decision boundary is a circle with radius 0.9 | 62.00% | 31 of 50 correct |
| 8 | k-nearest neighbors with k=3 | 0.00% | 0 of 50 correct |
| 9 | k-nearest neighbors with k=5 | 0.00% | 0 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [0.828, 0.352] | 1 | 1 | ✓ CORRECT |
| [0.463, -0.772] | 1 | 1 | ✓ CORRECT |
| [-0.146, -0.888] | 1 | 1 | ✓ CORRECT |
| [-0.212, -0.977] | 0 | 1 | ✗ WRONG |
| [-0.257, -0.863] | 1 | 1 | ✓ CORRECT |
| [0.138, -0.990] | 0 | 1 | ✗ WRONG |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 1 | ✓ CORRECT |
| [-0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 0 | ✗ WRONG |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | 0 | ✗ WRONG |
| [0.828, 0.352] | 1 | 0 | ✗ WRONG |
| [0.463, -0.772] | 1 | 0 | ✗ WRONG |
| [-0.146, -0.888] | 1 | 0 | ✗ WRONG |
| [-0.212, -0.977] | 0 | 0 | ✓ CORRECT |
| [-0.257, -0.863] | 1 | 0 | ✗ WRONG |
| [0.138, -0.990] | 0 | 0 | ✓ CORRECT |
| [0.034, 0.899] | 1 | 1 | ✓ CORRECT |
| [-0.877, 0.202] | 1 | 0 | ✗ WRONG |
| [-0.482, -0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [0.828, 0.352] | 1 | ERROR | ✗ WRONG |
| [0.463, -0.772] | 1 | ERROR | ✗ WRONG |
| [-0.146, -0.888] | 1 | ERROR | ✗ WRONG |
| [-0.212, -0.977] | 0 | ERROR | ✗ WRONG |
| [-0.257, -0.863] | 1 | ERROR | ✗ WRONG |
| [0.138, -0.990] | 0 | ERROR | ✗ WRONG |
| [0.034, 0.899] | 1 | ERROR | ✗ WRONG |
| [-0.877, 0.202] | 1 | ERROR | ✗ WRONG |
| [-0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 95 |
| split | test |
| Order | Model | Accuracy | Details |
|---|---|---|---|
| 0 | Quadrant-based classification | 44.00% | 22 of 50 correct |
| 1 | Angle-based classification | 0.00% | 0 of 50 correct |
| 2 | K-nearest neighbors with k=1 | 0.00% | 0 of 50 correct |
| 3 | K-nearest neighbors with k=3 | 0.00% | 0 of 50 correct |
| 4 | Y-threshold classification | 52.00% | 26 of 50 correct |
| 5 | Distance from origin classification | 100.00% | 50 of 50 correct |
| 6 | Final distance-based classification rule | 100.00% | 50 of 50 correct |
| 7 | Confirmed distance-based classification with edge cases | 100.00% | 50 of 50 correct |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | 1 | ✗ WRONG |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [0.482, -0.760] | 1 | 0 | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.482, 0.760] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.746, -0.666] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.482, 0.760] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.746, -0.666] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | ERROR | ✗ WRONG |
| [-0.520, -0.735] | 1 | ERROR | ✗ WRONG |
| [-0.482, 0.760] | 1 | ERROR | ✗ WRONG |
| [-0.778, -0.628] | 0 | ERROR | ✗ WRONG |
| [0.746, -0.666] | 0 | ERROR | ✗ WRONG |
| [-0.939, -0.345] | 0 | ERROR | ✗ WRONG |
| [-0.920, -0.391] | 0 | ERROR | ✗ WRONG |
| [0.514, 0.858] | 0 | ERROR | ✗ WRONG |
| [0.113, 0.994] | 0 | ERROR | ✗ WRONG |
| [0.482, -0.760] | 1 | ERROR | ✗ WRONG |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 1 | ✗ WRONG |
| [0.746, -0.666] | 0 | 1 | ✗ WRONG |
| [-0.939, -0.345] | 0 | 1 | ✗ WRONG |
| [-0.920, -0.391] | 0 | 1 | ✗ WRONG |
| [0.514, 0.858] | 0 | 1 | ✗ WRONG |
| [0.113, 0.994] | 0 | 1 | ✗ WRONG |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Features | True Label | Predicted | Result |
|---|---|---|---|
| [-0.493, -0.870] | 0 | 0 | ✓ CORRECT |
| [-0.520, -0.735] | 1 | 1 | ✓ CORRECT |
| [-0.482, 0.760] | 1 | 1 | ✓ CORRECT |
| [-0.778, -0.628] | 0 | 0 | ✓ CORRECT |
| [0.746, -0.666] | 0 | 0 | ✓ CORRECT |
| [-0.939, -0.345] | 0 | 0 | ✓ CORRECT |
| [-0.920, -0.391] | 0 | 0 | ✓ CORRECT |
| [0.514, 0.858] | 0 | 0 | ✓ CORRECT |
| [0.113, 0.994] | 0 | 0 | ✓ CORRECT |
| [0.482, -0.760] | 1 | 1 | ✓ CORRECT |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 96 |
| split | test |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 97 |
| split | test |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 98 |
| split | test |
| Index | Value |
|---|
| Property | Value |
|---|---|
| index | 99 |
| split | test |